{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compare tests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dftests = pd.DataFrame()\n",
    "#for method in ['bonobo','spcc','sweet','lioness']:\n",
    "#    dt = pd.read_csv('../results/pseudobulk_perc02/'+ 'gcn4_'+ method + '/'+ method+'_all_tests.tsv', sep='\\t')\n",
    "#    dt['method'] = method\n",
    "#    dftests = pd.concat([dftests,dt ], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dftests = pd.DataFrame()\n",
    "for method in ['spcc','bonobo','lioness','sweet']:#['bonobo','spcc','sweet','lioness']:\n",
    "    #results_folder + method+'_genomedia_tests_toponly.tsv'\n",
    "    dt = pd.read_csv('../results/pseudobulk_perc02/'+ 'gcn4_'+ method + '/'+ method+'_genomedia_topmedia_tests_toponly.tsv', sep='\\t')\n",
    "    #dt = pd.read_csv('../results/pseudobulk_perc02/'+ 'gcn4_'+ method + '/'+ method+'_genomedia_topwt_tests_toponly.tsv', sep='\\t')\n",
    "    \n",
    "    dt['method'] = method\n",
    "    dftests = pd.concat([dftests,dt ], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_folder = '../results/pseudobulk_perc02/'+ 'gcn4_genomedia_topwt_toponly_'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>perturbation</th>\n",
       "      <th>k_edges</th>\n",
       "      <th>stat_wilk</th>\n",
       "      <th>pval_wilk</th>\n",
       "      <th>stat_ks</th>\n",
       "      <th>pval_ks</th>\n",
       "      <th>stat_ks_other</th>\n",
       "      <th>pval_ks_other</th>\n",
       "      <th>method</th>\n",
       "      <th>logp_wilk</th>\n",
       "      <th>logp_ks_other</th>\n",
       "      <th>logp_ks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal80</td>\n",
       "      <td>AmmoniumSulfate</td>\n",
       "      <td>dal80</td>\n",
       "      <td>290</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>1.148609e-07</td>\n",
       "      <td>0.420690</td>\n",
       "      <td>2.210536e-23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>spcc</td>\n",
       "      <td>6.939828</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>22.655502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal80</td>\n",
       "      <td>CStarve</td>\n",
       "      <td>dal80</td>\n",
       "      <td>290</td>\n",
       "      <td>4783.0</td>\n",
       "      <td>3.551190e-30</td>\n",
       "      <td>0.762069</td>\n",
       "      <td>6.260641e-83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>spcc</td>\n",
       "      <td>29.449626</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>82.203381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dal80</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>dal80</td>\n",
       "      <td>290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.630577e-49</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.526137e-173</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>spcc</td>\n",
       "      <td>48.579949</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>172.816406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal80</td>\n",
       "      <td>MinimalEtOH</td>\n",
       "      <td>dal80</td>\n",
       "      <td>290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.630577e-49</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.526137e-173</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>spcc</td>\n",
       "      <td>48.579949</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>172.816406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dal80</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>dal80</td>\n",
       "      <td>290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.630577e-49</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.526137e-173</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>spcc</td>\n",
       "      <td>48.579949</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>172.816406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>stp2</td>\n",
       "      <td>Urea</td>\n",
       "      <td>rtg3</td>\n",
       "      <td>1160</td>\n",
       "      <td>237485.0</td>\n",
       "      <td>3.538128e-18</td>\n",
       "      <td>0.136207</td>\n",
       "      <td>8.504770e-10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sweet</td>\n",
       "      <td>17.451226</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>9.070337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3989</th>\n",
       "      <td>stp2</td>\n",
       "      <td>YPD</td>\n",
       "      <td>rtg3</td>\n",
       "      <td>1160</td>\n",
       "      <td>198504.0</td>\n",
       "      <td>9.532815e-34</td>\n",
       "      <td>0.018103</td>\n",
       "      <td>9.913231e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sweet</td>\n",
       "      <td>33.020779</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.003785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>stp2</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>rtg3</td>\n",
       "      <td>1160</td>\n",
       "      <td>294651.0</td>\n",
       "      <td>2.299411e-04</td>\n",
       "      <td>0.032759</td>\n",
       "      <td>5.624508e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sweet</td>\n",
       "      <td>3.638383</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.249915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>stp2</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>rtg3</td>\n",
       "      <td>1160</td>\n",
       "      <td>234577.0</td>\n",
       "      <td>3.634697e-19</td>\n",
       "      <td>0.299138</td>\n",
       "      <td>3.496473e-46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sweet</td>\n",
       "      <td>18.439532</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>45.456370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>stp2</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>rtg3</td>\n",
       "      <td>1160</td>\n",
       "      <td>254524.0</td>\n",
       "      <td>6.034011e-13</td>\n",
       "      <td>0.045690</td>\n",
       "      <td>1.774801e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>sweet</td>\n",
       "      <td>12.219394</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.750850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15972 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gene1            gene2 perturbation  k_edges  stat_wilk     pval_wilk  \\\n",
       "0     dal80  AmmoniumSulfate        dal80      290    13520.0  1.148609e-07   \n",
       "1     dal80          CStarve        dal80      290     4783.0  3.551190e-30   \n",
       "2     dal80        Glutamine        dal80      290        0.0  2.630577e-49   \n",
       "3     dal80      MinimalEtOH        dal80      290        0.0  2.630577e-49   \n",
       "4     dal80   MinimalGlucose        dal80      290        0.0  2.630577e-49   \n",
       "...     ...              ...          ...      ...        ...           ...   \n",
       "3988   stp2             Urea         rtg3     1160   237485.0  3.538128e-18   \n",
       "3989   stp2              YPD         rtg3     1160   198504.0  9.532815e-34   \n",
       "3990   stp2       YPDDiauxic         rtg3     1160   294651.0  2.299411e-04   \n",
       "3991   stp2          YPDRapa         rtg3     1160   234577.0  3.634697e-19   \n",
       "3992   stp2           YPEtOH         rtg3     1160   254524.0  6.034011e-13   \n",
       "\n",
       "       stat_ks        pval_ks  stat_ks_other  pval_ks_other method  logp_wilk  \\\n",
       "0     0.420690   2.210536e-23              0              1   spcc   6.939828   \n",
       "1     0.762069   6.260641e-83              0              1   spcc  29.449626   \n",
       "2     1.000000  1.526137e-173              0              1   spcc  48.579949   \n",
       "3     1.000000  1.526137e-173              0              1   spcc  48.579949   \n",
       "4     1.000000  1.526137e-173              0              1   spcc  48.579949   \n",
       "...        ...            ...            ...            ...    ...        ...   \n",
       "3988  0.136207   8.504770e-10              0              1  sweet  17.451226   \n",
       "3989  0.018103   9.913231e-01              0              1  sweet  33.020779   \n",
       "3990  0.032759   5.624508e-01              0              1  sweet   3.638383   \n",
       "3991  0.299138   3.496473e-46              0              1  sweet  18.439532   \n",
       "3992  0.045690   1.774801e-01              0              1  sweet  12.219394   \n",
       "\n",
       "      logp_ks_other     logp_ks  \n",
       "0              -0.0   22.655502  \n",
       "1              -0.0   82.203381  \n",
       "2              -0.0  172.816406  \n",
       "3              -0.0  172.816406  \n",
       "4              -0.0  172.816406  \n",
       "...             ...         ...  \n",
       "3988           -0.0    9.070337  \n",
       "3989           -0.0    0.003785  \n",
       "3990           -0.0    0.249915  \n",
       "3991           -0.0   45.456370  \n",
       "3992           -0.0    0.750850  \n",
       "\n",
       "[15972 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftests['logp_wilk'] = -np.log10(dftests['pval_wilk'])\n",
    "dftests['logp_ks_other'] = -np.log10(dftests['pval_ks_other'])\n",
    "dftests['logp_ks'] = -np.log10(dftests['pval_ks'])\n",
    "dftests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dftests.to_csv(results_folder + 'all_methods.tsv', sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>perturbation</th>\n",
       "      <th>k_edges</th>\n",
       "      <th>stat_wilk</th>\n",
       "      <th>pval_wilk</th>\n",
       "      <th>stat_ks</th>\n",
       "      <th>pval_ks</th>\n",
       "      <th>stat_ks_other</th>\n",
       "      <th>pval_ks_other</th>\n",
       "      <th>method</th>\n",
       "      <th>logp_wilk</th>\n",
       "      <th>logp_ks_other</th>\n",
       "      <th>logp_ks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [gene1, gene2, perturbation, k_edges, stat_wilk, pval_wilk, stat_ks, pval_ks, stat_ks_other, pval_ks_other, method, logp_wilk, logp_ks_other, logp_ks]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftests[dftests['k_edges']==50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#g1 = sns.catplot(data=dftests[dftests['gene1']=='all_edges'], x='perturbation', y='pval_wilk', hue = 'method', kind = 'bar', col = 'k_edges')\n",
    "#g2 = sns.catplot(data=dftests[dftests['gene1']=='all_edges'], x='perturbation', y='pval_ks', hue = 'method', kind = 'bar', col = 'k_edges')\n",
    "#g3 = sns.catplot(data=dftests[dftests['gene1']=='all_edges'], x='perturbation', y='logp_ks_other', hue = 'method', kind = 'bar')\n",
    "#g1.fig.savefig(results_folder + 'all_edges_tests_wilk_pairedwt.pdf')\n",
    "#g2.fig.savefig(results_folder + 'all_edges_tests_ks_wt.pdf')\n",
    "#g3.fig.savefig(results_folder + 'all_edges_tests_ks_other.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f, ax = f,ax = plt.subplots(1,figsize = (10,10))\n",
    "#hue_order = ['bonobo','spcc','sweet','lioness']\n",
    "#sns.catplot(data=dftests, y='perturbation', x='pval_ks_other', hue='method', dodge=True, kind = 'box', aspect = .5, height=5, col = 'k_edges', hue_order = hue_order)\n",
    "#sns.catplot(data=dftests, y='perturbation', x='pval_ks', hue='method', dodge=True, kind = 'box', aspect = .5, height=5, col = 'k_edges', hue_order=hue_order)\n",
    "#sns.catplot(data=dftests, y='perturbation', x='pval_wilk', hue='method', dodge=True, kind = 'box', aspect = .5, height=5, col = 'k_edges', hue_order=hue_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f, ax = f,ax = plt.subplots(1,3,figsize = (15,8))\n",
    "#th = 0.05\n",
    "#k = 50\n",
    "#datak = dftests[dftests['k_edges']==k]\n",
    "#sns.countplot(data=datak[datak['pval_ks_other']<th], y='perturbation', hue='method', dodge=True,stat = 'count', ax=ax[0])\n",
    "#sns.countplot(data=datak[datak['pval_ks']<th], y='perturbation', hue='method', dodge=True,stat = 'count', ax=ax[1])\n",
    "#sns.countplot(data=datak[datak['pval_wilk']<th], y='perturbation', hue='method', dodge=True,stat = 'count', ax=ax[2])\n",
    "#f.savefig(results_folder + 'count_significant_th01_tests.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nsig = datak[datak['pval_ks_other']<th].groupby(['perturbation','method']).size().unstack()\n",
    "#nsig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kolmogorov smirnov test\n",
    "\n",
    "#for i in nsig.columns:\n",
    "#    for j in nsig.columns:\n",
    "#        if i!=j:\n",
    "#            print(i,j)\n",
    "#            print(stats.wilcoxon(nsig[i],nsig[j], alternative = 'greater'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dftests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonobo\n",
      "dal80\n",
      "290 dal80 bonobo 0.48076923076923084\n",
      "lioness\n",
      "dal80\n",
      "290 dal80 lioness 0.668693009118541\n",
      "spcc\n",
      "dal80\n",
      "290 dal80 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal80\n",
      "290 dal80 sweet 0.6130268199233716\n",
      "bonobo\n",
      "dal81\n",
      "290 dal81 bonobo 0.656934306569343\n",
      "lioness\n",
      "dal81\n",
      "290 dal81 lioness 0.6666666666666666\n",
      "spcc\n",
      "dal81\n",
      "290 dal81 spcc 0.668693009118541\n",
      "sweet\n",
      "dal81\n",
      "290 dal81 sweet 0.6405693950177936\n",
      "bonobo\n",
      "dal82\n",
      "290 dal82 bonobo 0.6474820143884892\n",
      "lioness\n",
      "dal82\n",
      "290 dal82 lioness 0.668693009118541\n",
      "spcc\n",
      "dal82\n",
      "290 dal82 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal82\n",
      "290 dal82 sweet 0.6360424028268552\n",
      "bonobo\n",
      "gat1\n",
      "290 gat1 bonobo 0.7258064516129031\n",
      "lioness\n",
      "gat1\n",
      "290 gat1 lioness 0.6666666666666666\n",
      "spcc\n",
      "gat1\n",
      "290 gat1 spcc 0.625\n",
      "sweet\n",
      "gat1\n",
      "290 gat1 sweet 0.6818181818181819\n",
      "bonobo\n",
      "gcn4\n",
      "290 gcn4 bonobo 0.8661417322834646\n",
      "lioness\n",
      "gcn4\n",
      "290 gcn4 lioness 0.6666666666666666\n",
      "spcc\n",
      "gcn4\n",
      "290 gcn4 spcc 0.668693009118541\n",
      "sweet\n",
      "gcn4\n",
      "290 gcn4 sweet 0.7586206896551725\n",
      "bonobo\n",
      "gln3\n",
      "290 gln3 bonobo 0.7333333333333333\n",
      "lioness\n",
      "gln3\n",
      "290 gln3 lioness 0.6666666666666666\n",
      "spcc\n",
      "gln3\n",
      "290 gln3 spcc 0.6666666666666666\n",
      "sweet\n",
      "gln3\n",
      "290 gln3 sweet 0.6711409395973155\n",
      "bonobo\n",
      "gzf3\n",
      "290 gzf3 bonobo 0.6349206349206349\n",
      "lioness\n",
      "gzf3\n",
      "290 gzf3 lioness 0.6666666666666666\n",
      "spcc\n",
      "gzf3\n",
      "290 gzf3 spcc 0.6666666666666666\n",
      "sweet\n",
      "gzf3\n",
      "290 gzf3 sweet 0.6428571428571429\n",
      "bonobo\n",
      "rtg1\n",
      "290 rtg1 bonobo 0.5714285714285714\n",
      "lioness\n",
      "rtg1\n",
      "290 rtg1 lioness 0.6666666666666666\n",
      "spcc\n",
      "rtg1\n",
      "290 rtg1 spcc 0.6666666666666666\n",
      "sweet\n",
      "rtg1\n",
      "290 rtg1 sweet 0.6315789473684211\n",
      "bonobo\n",
      "rtg3\n",
      "290 rtg3 bonobo 0.7092198581560284\n",
      "lioness\n",
      "rtg3\n",
      "290 rtg3 lioness 0.668693009118541\n",
      "spcc\n",
      "rtg3\n",
      "290 rtg3 spcc 0.6666666666666666\n",
      "sweet\n",
      "rtg3\n",
      "290 rtg3 sweet 0.6920415224913495\n",
      "bonobo\n",
      "stp1\n",
      "290 stp1 bonobo 0.5970149253731343\n",
      "lioness\n",
      "stp1\n",
      "290 stp1 lioness 0.668693009118541\n",
      "spcc\n",
      "stp1\n",
      "290 stp1 spcc 0.6666666666666666\n",
      "sweet\n",
      "stp1\n",
      "290 stp1 sweet 0.6008583690987125\n",
      "bonobo\n",
      "stp2\n",
      "290 stp2 bonobo 0.7200000000000001\n",
      "lioness\n",
      "stp2\n",
      "290 stp2 lioness 0.6666666666666666\n",
      "spcc\n",
      "stp2\n",
      "290 stp2 spcc 0.6666666666666666\n",
      "sweet\n",
      "stp2\n",
      "290 stp2 sweet 0.6741573033707866\n",
      "bonobo\n",
      "dal80\n",
      "580 dal80 bonobo 0.622568093385214\n",
      "lioness\n",
      "dal80\n",
      "580 dal80 lioness 0.668693009118541\n",
      "spcc\n",
      "dal80\n",
      "580 dal80 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal80\n",
      "580 dal80 sweet 0.6037735849056604\n",
      "bonobo\n",
      "dal81\n",
      "580 dal81 bonobo 0.6521739130434783\n",
      "lioness\n",
      "dal81\n",
      "580 dal81 lioness 0.6666666666666666\n",
      "spcc\n",
      "dal81\n",
      "580 dal81 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal81\n",
      "580 dal81 sweet 0.6315789473684211\n",
      "bonobo\n",
      "dal82\n",
      "580 dal82 bonobo 0.6474820143884892\n",
      "lioness\n",
      "dal82\n",
      "580 dal82 lioness 0.6666666666666666\n",
      "spcc\n",
      "dal82\n",
      "580 dal82 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal82\n",
      "580 dal82 sweet 0.6756756756756757\n",
      "bonobo\n",
      "gat1\n",
      "580 gat1 bonobo 0.7575757575757576\n",
      "lioness\n",
      "gat1\n",
      "580 gat1 lioness 0.6666666666666666\n",
      "spcc\n",
      "gat1\n",
      "580 gat1 spcc 0.625\n",
      "sweet\n",
      "gat1\n",
      "580 gat1 sweet 0.6593406593406593\n",
      "bonobo\n",
      "gcn4\n",
      "580 gcn4 bonobo 0.8\n",
      "lioness\n",
      "gcn4\n",
      "580 gcn4 lioness 0.6666666666666666\n",
      "spcc\n",
      "gcn4\n",
      "580 gcn4 spcc 0.6666666666666666\n",
      "sweet\n",
      "gcn4\n",
      "580 gcn4 sweet 0.7407407407407407\n",
      "bonobo\n",
      "gln3\n",
      "580 gln3 bonobo 0.7308970099667774\n",
      "lioness\n",
      "gln3\n",
      "580 gln3 lioness 0.6666666666666666\n",
      "spcc\n",
      "gln3\n",
      "580 gln3 spcc 0.6666666666666666\n",
      "sweet\n",
      "gln3\n",
      "580 gln3 sweet 0.6600660066006601\n",
      "bonobo\n",
      "gzf3\n",
      "580 gzf3 bonobo 0.6153846153846154\n",
      "lioness\n",
      "gzf3\n",
      "580 gzf3 lioness 0.6666666666666666\n",
      "spcc\n",
      "gzf3\n",
      "580 gzf3 spcc 0.6666666666666666\n",
      "sweet\n",
      "gzf3\n",
      "580 gzf3 sweet 0.6405693950177936\n",
      "bonobo\n",
      "rtg1\n",
      "580 rtg1 bonobo 0.6474820143884892\n",
      "lioness\n",
      "rtg1\n",
      "580 rtg1 lioness 0.6666666666666666\n",
      "spcc\n",
      "rtg1\n",
      "580 rtg1 spcc 0.6666666666666666\n",
      "sweet\n",
      "rtg1\n",
      "580 rtg1 sweet 0.6293706293706294\n",
      "bonobo\n",
      "rtg3\n",
      "580 rtg3 bonobo 0.6896551724137931\n",
      "lioness\n",
      "rtg3\n",
      "580 rtg3 lioness 0.6666666666666666\n",
      "spcc\n",
      "rtg3\n",
      "580 rtg3 spcc 0.6666666666666666\n",
      "sweet\n",
      "rtg3\n",
      "580 rtg3 sweet 0.68259385665529\n",
      "bonobo\n",
      "stp1\n",
      "580 stp1 bonobo 0.6542056074766356\n",
      "lioness\n",
      "stp1\n",
      "580 stp1 lioness 0.668693009118541\n",
      "spcc\n",
      "stp1\n",
      "580 stp1 spcc 0.6666666666666666\n",
      "sweet\n",
      "stp1\n",
      "580 stp1 sweet 0.6299212598425197\n",
      "bonobo\n",
      "stp2\n",
      "580 stp2 bonobo 0.6451612903225807\n",
      "lioness\n",
      "stp2\n",
      "580 stp2 lioness 0.668693009118541\n",
      "spcc\n",
      "stp2\n",
      "580 stp2 spcc 0.6666666666666666\n",
      "sweet\n",
      "stp2\n",
      "580 stp2 sweet 0.6177606177606177\n",
      "bonobo\n",
      "dal80\n",
      "1160 dal80 bonobo 0.661764705882353\n",
      "lioness\n",
      "dal80\n",
      "1160 dal80 lioness 0.6666666666666666\n",
      "spcc\n",
      "dal80\n",
      "1160 dal80 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal80\n",
      "1160 dal80 sweet 0.5970149253731343\n",
      "bonobo\n",
      "dal81\n",
      "1160 dal81 bonobo 0.6428571428571429\n",
      "lioness\n",
      "dal81\n",
      "1160 dal81 lioness 0.6666666666666666\n",
      "spcc\n",
      "dal81\n",
      "1160 dal81 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal81\n",
      "1160 dal81 sweet 0.625\n",
      "bonobo\n",
      "dal82\n",
      "1160 dal82 bonobo 0.6451612903225807\n",
      "lioness\n",
      "dal82\n",
      "1160 dal82 lioness 0.6666666666666666\n",
      "spcc\n",
      "dal82\n",
      "1160 dal82 spcc 0.6666666666666666\n",
      "sweet\n",
      "dal82\n",
      "1160 dal82 sweet 0.6756756756756757\n",
      "bonobo\n",
      "gat1\n",
      "1160 gat1 bonobo 0.7272727272727273\n",
      "lioness\n",
      "gat1\n",
      "1160 gat1 lioness 0.6666666666666666\n",
      "spcc\n",
      "gat1\n",
      "1160 gat1 spcc 0.6666666666666666\n",
      "sweet\n",
      "gat1\n",
      "1160 gat1 sweet 0.6521739130434783\n",
      "bonobo\n",
      "gcn4\n",
      "1160 gcn4 bonobo 0.7773851590106007\n",
      "lioness\n",
      "gcn4\n",
      "1160 gcn4 lioness 0.6666666666666666\n",
      "spcc\n",
      "gcn4\n",
      "1160 gcn4 spcc 0.6666666666666666\n",
      "sweet\n",
      "gcn4\n",
      "1160 gcn4 sweet 0.7432432432432432\n",
      "bonobo\n",
      "gln3\n",
      "1160 gln3 bonobo 0.7189542483660131\n",
      "lioness\n",
      "gln3\n",
      "1160 gln3 lioness 0.6666666666666666\n",
      "spcc\n",
      "gln3\n",
      "1160 gln3 spcc 0.6666666666666666\n",
      "sweet\n",
      "gln3\n",
      "1160 gln3 sweet 0.694006309148265\n",
      "bonobo\n",
      "gzf3\n",
      "1160 gzf3 bonobo 0.656934306569343\n",
      "lioness\n",
      "gzf3\n",
      "1160 gzf3 lioness 0.6666666666666666\n",
      "spcc\n",
      "gzf3\n",
      "1160 gzf3 spcc 0.6666666666666666\n",
      "sweet\n",
      "gzf3\n",
      "1160 gzf3 sweet 0.6338028169014085\n",
      "bonobo\n",
      "rtg1\n",
      "1160 rtg1 bonobo 0.6711409395973155\n",
      "lioness\n",
      "rtg1\n",
      "1160 rtg1 lioness 0.6666666666666666\n",
      "spcc\n",
      "rtg1\n",
      "1160 rtg1 spcc 0.6666666666666666\n",
      "sweet\n",
      "rtg1\n",
      "1160 rtg1 sweet 0.625\n",
      "bonobo\n",
      "rtg3\n",
      "1160 rtg3 bonobo 0.7142857142857143\n",
      "lioness\n",
      "rtg3\n",
      "1160 rtg3 lioness 0.6666666666666666\n",
      "spcc\n",
      "rtg3\n",
      "1160 rtg3 spcc 0.6666666666666666\n",
      "sweet\n",
      "rtg3\n",
      "1160 rtg3 sweet 0.7213114754098361\n",
      "bonobo\n",
      "stp1\n",
      "1160 stp1 bonobo 0.6451612903225807\n",
      "lioness\n",
      "stp1\n",
      "1160 stp1 lioness 0.6666666666666666\n",
      "spcc\n",
      "stp1\n",
      "1160 stp1 spcc 0.6666666666666666\n",
      "sweet\n",
      "stp1\n",
      "1160 stp1 sweet 0.6642066420664207\n",
      "bonobo\n",
      "stp2\n",
      "1160 stp2 bonobo 0.6741573033707866\n",
      "lioness\n",
      "stp2\n",
      "1160 stp2 lioness 0.6666666666666666\n",
      "spcc\n",
      "stp2\n",
      "1160 stp2 spcc 0.6666666666666666\n",
      "sweet\n",
      "stp2\n",
      "1160 stp2 sweet 0.5947955390334573\n"
     ]
    }
   ],
   "source": [
    "th = 0.05\n",
    "\n",
    "df_f1 = []\n",
    "for kkk in dftests['k_edges'].unique():\n",
    "    datak = dftests[dftests['k_edges']==kkk]\n",
    "    datak = datak[datak['gene1']!='all_edges']\n",
    "    for ppp,tab0 in datak.groupby('perturbation'):\n",
    "        for meth,tab in tab0.groupby('method'):\n",
    "            print(meth)\n",
    "            print(ppp)\n",
    "            tab_same = tab[tab['gene1']==tab['perturbation']]\n",
    "            tab_diff = tab[tab['gene1']!=tab['perturbation']]\n",
    "            eval_TP  = len(tab_same[tab_same['pval_ks']<th])/len(tab_same)\n",
    "            eval_FP = len(tab_diff[tab_diff['pval_ks']<th])/len(tab_diff)\n",
    "            # Compute F1 score = TP / [TP + 0.5*(FP + FN)]\n",
    "            # false positive = 1-TN\n",
    "            eval_TN = 1-eval_FP\n",
    "            # false negative = 1-TP\n",
    "            eval_FN = 1-eval_TP\n",
    "            F1 = eval_TP / (eval_TP + 0.5*(eval_FP + eval_FN))\n",
    "            print(kkk,ppp, meth, F1)\n",
    "            # add results to df_f1\n",
    "            df_f1.append([kkk,ppp,meth,F1, eval_TP, eval_FP])\n",
    "            \n",
    "            \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f1 = pd.DataFrame(data = df_f1, columns = ['k_edges','perturbation','method','f1','tp','fp'])\n",
    "#df_f1.to_csv(results_folder + 'all_f1.tsv', sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k_edges</th>\n",
       "      <th>perturbation</th>\n",
       "      <th>method</th>\n",
       "      <th>f1</th>\n",
       "      <th>tp</th>\n",
       "      <th>fp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>290</td>\n",
       "      <td>dal80</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>0.480769</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.436364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290</td>\n",
       "      <td>dal80</td>\n",
       "      <td>lioness</td>\n",
       "      <td>0.668693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>290</td>\n",
       "      <td>dal80</td>\n",
       "      <td>spcc</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>290</td>\n",
       "      <td>dal80</td>\n",
       "      <td>sweet</td>\n",
       "      <td>0.613027</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.645455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>290</td>\n",
       "      <td>dal81</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>0.656934</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.672727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1160</td>\n",
       "      <td>stp1</td>\n",
       "      <td>sweet</td>\n",
       "      <td>0.664207</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.645455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1160</td>\n",
       "      <td>stp2</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>0.674157</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.609091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1160</td>\n",
       "      <td>stp2</td>\n",
       "      <td>lioness</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1160</td>\n",
       "      <td>stp2</td>\n",
       "      <td>spcc</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1160</td>\n",
       "      <td>stp2</td>\n",
       "      <td>sweet</td>\n",
       "      <td>0.594796</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.718182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     k_edges perturbation   method        f1        tp        fp\n",
       "0        290        dal80   bonobo  0.480769  0.454545  0.436364\n",
       "1        290        dal80  lioness  0.668693  1.000000  0.990909\n",
       "2        290        dal80     spcc  0.666667  1.000000  1.000000\n",
       "3        290        dal80    sweet  0.613027  0.727273  0.645455\n",
       "4        290        dal81   bonobo  0.656934  0.818182  0.672727\n",
       "..       ...          ...      ...       ...       ...       ...\n",
       "127     1160         stp1    sweet  0.664207  0.818182  0.645455\n",
       "128     1160         stp2   bonobo  0.674157  0.818182  0.609091\n",
       "129     1160         stp2  lioness  0.666667  1.000000  1.000000\n",
       "130     1160         stp2     spcc  0.666667  1.000000  1.000000\n",
       "131     1160         stp2    sweet  0.594796  0.727273  0.718182\n",
       "\n",
       "[132 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1634.5x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook', font_scale = 1.2)\n",
    "g1 = sns.catplot(data = df_f1, col = 'k_edges', y = 'f1', x='perturbation', hue = 'method', kind = 'point')\n",
    "# rotate xticks\n",
    "g1.set_xticklabels(rotation = 45)\n",
    "g1.fig.savefig('../results/f1_comparisons.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb46fb554f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1602x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data = df_f1, col = 'k_edges', x = 'f1', y='perturbation', hue = 'method', kind = 'bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bonobo-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
